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Automated Signal Generation in Prescription-Event Monitoring

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Abstract

Signal generation is a method of highlighting potential safety issues in a drug that then need to be investigated further. Previously automated signal generation has mainly been applied to spontaneous reporting systems. The Drug Safety Research Unit (DSRU) performs observational postmarketing studies on selected newly marketed medicines in England using a method known as prescription-event monitoring (PEM). The DSRU has investigated automated procedures for the generation of signals using the event data from PEM studies.

Proportional reporting ratios (PRRs) and incidence rate ratios (IRRs) were studied as possible tools for signal generation in PEM data. The PEM database contains 78 completed studies of drugs prescribed in primary care from a variety of therapeutic classes. Retrospective studies were carried out to identify the implications of changing the comparator group of drugs, along with analysing the results at different levels in the DSRU’s hierarchical dictionary and performing signal generation after 30 and 180 days of observation since starting the drug.

Automated signal generation is a useful hypothesis generating method that is likely to prove to be useful both in clinical trials and postmarketing studies. PRRs are simple to apply and do not require a denominator. IRRs take into account the time subjects were exposed to the drug prior to the event of interest, and offers a useful, and more in depth look into the data. However, with both methods it is important to perform signal generation at multiple levels in the dictionary and with careful selection of the comparator group.

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Acknowledgements

We thank Professor Stephen Evans for his invaluable advice on the statistical analysis. Along with the AstraZeneca foundation for the research grant for Dr Emma Heeley, we are also very grateful to the general practitioners who voluntarily take part in prescription-event monitoring studies.

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Authors and Affiliations

  1. Drug Safety Research Unit, Bursledon Hall, Blundell Lane, Southampton, SO31 1AA, United Kingdom

    Emma Heeley, Lynda V. Wilton & Saad A.W. Shakir

  2. London School of Hygiene and Tropical Medicine, Public Health and Policy, London, United Kingdom

    Saad A.W. Shakir

Authors
  1. Emma Heeley
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  2. Lynda V. Wilton
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  3. Saad A.W. Shakir
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Correspondence to Emma Heeley.

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Heeley, E., Wilton, L.V. & Shakir, S.A. Automated Signal Generation in Prescription-Event Monitoring. Drug-Safety 25, 423–432 (2002). https://doi.org/10.2165/00002018-200225060-00006

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